Prediction Models for Celiac Disease Development in Children From High-Risk Families: Data From the PreventCD Cohort
Bibliographic record
Abstract
BACKGROUND & AIMS: Screening for celiac disease (CD) is recommended in children with affected first-degree relatives (FDR). However, the frequency of screening and at what age remain unknown. The aims of this study were to detect variables influencing the risk of CD development and develop and validate clinical prediction models to provide individualized screening advice. METHODS: We analyzed prospective data from the 10 years of follow-up of the PreventCD-birth cohort involving 944 genetically predisposed children with CD-FDR. Variables significantly influencing the CD risk were combined to determine a risk score. Landmark analyses were performed at different ages. Prediction models were created using multivariable Cox proportional hazards regression analyses, backward elimination, and Harrell's c-index for discrimination. Validation was done using data from the independent NeoCel cohort. RESULTS: In March 2019, the median follow-up was 8.3 years (22 days-12.0 years); 135/944 children developed CD (mean age, 4.3 years [range, 1.1-11.4]). CD developed significantly more often in girls (P = .005) and in Human Leukocyte Antigen (HLA)-DQ2 homozygous individuals (8-year cumulative incidence rate of 35.4% vs maximum of the other HLA-risk groups 18.2% [P < .001]). The effect of homozygosity DR3-DQ2/DR7-DQ2 on CD development was only present in girls (interaction P = .04). The prediction models showed good fit in the validation cohort (Cox regression 0.81 [0.54]). To calculate a personalized risk of CD development and provide screening advice, we designed the Prediction application https://hputter.shinyapps.io/preventcd/. CONCLUSION: Children with CD-FDR develop CD early in life, and their risk depends on gender, age and HLA-DQ, which are all factors that are important for sound screening advice. These children should be screened early in life, including HLA-DQ2/8-typing, and if genetically predisposed to CD, they should get further personalized screening advice using our Prediction application. TRIAL REGISTRATION NUMBER: ISRCTN74582487 (https://www.isrctn.com/search?q=ISRCTN74582487).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".